Current studies have developed various methods that focus on automatically ranking source files for given bug reports. Kim et al. [1]} applied a two-phase machine learning approach to predict files that could fix a bug report. DebugAdvisor [2]} used searches over structured and unstructured text to recommend a set of similar past tasks and the files that were changed. Saha et al. [3]} presented BLUiR and used the structure of bug reports and source code files to build a retrieval structure for reporting the bugs. Nguyen et al. [4]} applied LDA model to predict buggy files for given bug reports. Similarly, Lukins et al. [5]} applied LDA to predict and filter the top 10 defective files across 22% of overall bug reports. Ye et al. [6]} used developer domain knowledge (e.g., API documents) to identify source files associated to a bug. The same authors proposed a ranking model to recommend top 10 source files [7]}. Zhou et al. [8]} used VSM model to recommend textual similarities among similar past bugs and their associated files to address a new bug. Our approach instead recommends similar past bugs by extracting relevant bug-fixing comments from lengthy discussion threads of past bugs to address a new bug. A line of research by Runeson et al. [9]}, Wang et al. [10]}, Bettengurg et al. [11]}, Sun et al. [12]}, Hindle and Onuczko [13]} focused on detecting duplicate reports by querying bug reports.
